Excited to share that I’ll be attending the SIAM Conference on Uncertainty Quantification (UQ26) this year! 🎙️ At the conference, I will deliver an invited talk on Physics-Informed Posterior Sampling for Scientific Inverse Problems. Time: Tuesday 3 PM Link: Many scientific inverse problems ask us to reconstruct complex hidden fields from sparse, noisy observations. In these settings, a single best guess is often not enough. What we really need is a way to generate solutions that are both uncertainty-aware and faithful to the underlying physics. 💫 In this talk, I'll present a central message: Function-space diffusion can serve as a foundation for posterior sampling; decoupling prior and physics fixes the low-data bottleneck; and the resulting framework works in realistic scientific applications with verified posterior. I’m especially looking forward to sharing and learning new theories and advances. 🧸 If you’re attending UQ26, I would love to connect! #ScientificMachineLearning# #UncertaintyQuantification# #InverseProblems# #DiffusionModels# #PhysicsInformedAI# #BayesianInference# #MachineLearning#